Advanced Ensemble Techniques for IoT Cybersecurity: A Performance Comparison on the CICIoT2023 DatasetHora, Simran Kaurenys, AntanasGoranin, NikolajVilnius University Proceedings
(2023). Machine Learning Techniques for Anomaly-Based Detection System on CSE-CIC-IDS2018 Dataset. In: Berta, R., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2022. Lecture Notes in Electrical Engineering, vol 1036. Springer, Cham. ...
The goal of this paper is to construct an intrusion detection system using deep learning algorithms based on a new dataset named the CICIoT2023. The proposed intrusion detection model addresses challenges associated with intrusion detection datasets in terms of high dimensionality by adopting new ...
CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT EnvironmentINTERNET of thingsDENIAL of service attacksNowadays, the Internet of Things (IoT) concept plays a pivotal role in society and brings new capabilities to different industries. The number of IoT solutions in ...
In the case of IOT datasets, machine learning efficacy models critically rely on feature engineering. In this paper, we look more closely at the CIC-IOT-2023 Dataset and use clever feature engineering to squeeze out all its predictive power. The 2023 CIC IoT Dataset is comprised of network ...
We perform, document, and collect data from 33 attacks divided into 7 classes against IoT devices and demonstrated how they can be reproduced; We evaluate the performance of machine and deep learning algorithms using the CICIoT2023 dataset to classify and detect IoT network traffic as malicious or...
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Explore and run machine learning code with Kaggle Notebooks | Using data from UNB CIC IOT 2023 Dataset